{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  },
  "orig_nbformat": 4,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.4 64-bit"
  },
  "interpreter": {
   "hash": "7ad59aab27c906dbbf0c2e1dfcaac35fb186b016969018ec3a977f4c21569b21"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd "
   ]
  },
  {
   "source": [
    "1、数据加载， pd.read_excel('./18级高一体测成绩汇总.xls')默认加载第一个工作表"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     班级 性别 男1000米跑  男50米跑    男跳远  男体前屈  男引体  男肺活量     身高    体重  BMI\n",
       "0     1  男    4'13   8.88  195.0    12    1  2785  170.0  72.6    0\n",
       "1     1  男    4'16   7.70  225.0    11    7  3133  174.0  52.7    0\n",
       "2     1  男    4'09   8.45  218.0    14    1  3901  169.0  46.5    0\n",
       "3     1  男    4'21   8.05  206.0    13    1  4946  183.0  79.7    0\n",
       "4     1  男    3'44   7.52  210.0    13    9  3538  171.0  54.7    0\n",
       "..   .. ..     ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "472  17  男    4'23   8.27  208.0    10    0  4647  176.0  69.5    0\n",
       "473  17  男    5'19   9.55  210.0    15    6  7042  177.0  76.0    0\n",
       "474  17  男    3'25   7.50  252.0    13   13  5755  181.0  65.0    0\n",
       "475  17  男    4'39   7.81  208.0    14   11  5688  172.0  51.7    0\n",
       "476  17  男       0   0.00    0.0     0    0     0    0.0   0.0    0\n",
       "\n",
       "[477 rows x 11 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>班级</th>\n      <th>性别</th>\n      <th>男1000米跑</th>\n      <th>男50米跑</th>\n      <th>男跳远</th>\n      <th>男体前屈</th>\n      <th>男引体</th>\n      <th>男肺活量</th>\n      <th>身高</th>\n      <th>体重</th>\n      <th>BMI</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4'13</td>\n      <td>8.88</td>\n      <td>195.0</td>\n      <td>12</td>\n      <td>1</td>\n      <td>2785</td>\n      <td>170.0</td>\n      <td>72.6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4'16</td>\n      <td>7.70</td>\n      <td>225.0</td>\n      <td>11</td>\n      <td>7</td>\n      <td>3133</td>\n      <td>174.0</td>\n      <td>52.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4'09</td>\n      <td>8.45</td>\n      <td>218.0</td>\n      <td>14</td>\n      <td>1</td>\n      <td>3901</td>\n      <td>169.0</td>\n      <td>46.5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4'21</td>\n      <td>8.05</td>\n      <td>206.0</td>\n      <td>13</td>\n      <td>1</td>\n      <td>4946</td>\n      <td>183.0</td>\n      <td>79.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>男</td>\n      <td>3'44</td>\n      <td>7.52</td>\n      <td>210.0</td>\n      <td>13</td>\n      <td>9</td>\n      <td>3538</td>\n      <td>171.0</td>\n      <td>54.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>472</th>\n      <td>17</td>\n      <td>男</td>\n      <td>4'23</td>\n      <td>8.27</td>\n      <td>208.0</td>\n      <td>10</td>\n      <td>0</td>\n      <td>4647</td>\n      <td>176.0</td>\n      <td>69.5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>473</th>\n      <td>17</td>\n      <td>男</td>\n      <td>5'19</td>\n      <td>9.55</td>\n      <td>210.0</td>\n      <td>15</td>\n      <td>6</td>\n      <td>7042</td>\n      <td>177.0</td>\n      <td>76.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>474</th>\n      <td>17</td>\n      <td>男</td>\n      <td>3'25</td>\n      <td>7.50</td>\n      <td>252.0</td>\n      <td>13</td>\n      <td>13</td>\n      <td>5755</td>\n      <td>181.0</td>\n      <td>65.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>475</th>\n      <td>17</td>\n      <td>男</td>\n      <td>4'39</td>\n      <td>7.81</td>\n      <td>208.0</td>\n      <td>14</td>\n      <td>11</td>\n      <td>5688</td>\n      <td>172.0</td>\n      <td>51.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>476</th>\n      <td>17</td>\n      <td>男</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>477 rows × 11 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 58
    }
   ],
   "source": [
    "chengji_boy = pd.read_excel('./18级高一体测成绩汇总.xls',sheet_name=0,header=0)\n",
    "chengji_boy\n"
   ]
  },
  {
   "source": [
    "2、数据加载， pd.read_excel('./18级高一体测成绩汇总.xls',sheet_name = 1)指定加载第二个工作表"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     班级 性别  女800米跑  女50米跑    女跳远  女体前屈  女仰卧  女肺活量     身高    体重  BMI\n",
       "0     1  女    3.22   9.32  185.0    16   48  3775  163.0  51.3    0\n",
       "1     1  女    4.59  11.44  148.0     9   29  3683  163.0  66.6    0\n",
       "2     1  女    3.46  13.40  150.0     7   40  3331  157.0  60.0    0\n",
       "3     1  女    3.39   9.52  172.0    21   46  3701  160.0  50.7    0\n",
       "4     1  女    3.43   9.79  145.0     8   34  3592  167.0  63.9    0\n",
       "..   .. ..     ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "588  17  女    3.51   9.60  150.0    24   41  2255  158.0  49.0    0\n",
       "589  17  女    4.00  10.18  150.0    13   36  2937  161.0  55.7    0\n",
       "590  17  女    3.45  10.18  152.0    15   35  2592  165.0  48.6    0\n",
       "591  17  女    4.01   9.67  165.0    10   41  1829  154.0  43.6    0\n",
       "592  17  女    4.48   9.09  180.0    10   46  2962  162.0  55.3    0\n",
       "\n",
       "[593 rows x 11 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>班级</th>\n      <th>性别</th>\n      <th>女800米跑</th>\n      <th>女50米跑</th>\n      <th>女跳远</th>\n      <th>女体前屈</th>\n      <th>女仰卧</th>\n      <th>女肺活量</th>\n      <th>身高</th>\n      <th>体重</th>\n      <th>BMI</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>女</td>\n      <td>3.22</td>\n      <td>9.32</td>\n      <td>185.0</td>\n      <td>16</td>\n      <td>48</td>\n      <td>3775</td>\n      <td>163.0</td>\n      <td>51.3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>女</td>\n      <td>4.59</td>\n      <td>11.44</td>\n      <td>148.0</td>\n      <td>9</td>\n      <td>29</td>\n      <td>3683</td>\n      <td>163.0</td>\n      <td>66.6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>女</td>\n      <td>3.46</td>\n      <td>13.40</td>\n      <td>150.0</td>\n      <td>7</td>\n      <td>40</td>\n      <td>3331</td>\n      <td>157.0</td>\n      <td>60.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>女</td>\n      <td>3.39</td>\n      <td>9.52</td>\n      <td>172.0</td>\n      <td>21</td>\n      <td>46</td>\n      <td>3701</td>\n      <td>160.0</td>\n      <td>50.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>女</td>\n      <td>3.43</td>\n      <td>9.79</td>\n      <td>145.0</td>\n      <td>8</td>\n      <td>34</td>\n      <td>3592</td>\n      <td>167.0</td>\n      <td>63.9</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>588</th>\n      <td>17</td>\n      <td>女</td>\n      <td>3.51</td>\n      <td>9.60</td>\n      <td>150.0</td>\n      <td>24</td>\n      <td>41</td>\n      <td>2255</td>\n      <td>158.0</td>\n      <td>49.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>589</th>\n      <td>17</td>\n      <td>女</td>\n      <td>4.00</td>\n      <td>10.18</td>\n      <td>150.0</td>\n      <td>13</td>\n      <td>36</td>\n      <td>2937</td>\n      <td>161.0</td>\n      <td>55.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>590</th>\n      <td>17</td>\n      <td>女</td>\n      <td>3.45</td>\n      <td>10.18</td>\n      <td>152.0</td>\n      <td>15</td>\n      <td>35</td>\n      <td>2592</td>\n      <td>165.0</td>\n      <td>48.6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>591</th>\n      <td>17</td>\n      <td>女</td>\n      <td>4.01</td>\n      <td>9.67</td>\n      <td>165.0</td>\n      <td>10</td>\n      <td>41</td>\n      <td>1829</td>\n      <td>154.0</td>\n      <td>43.6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>592</th>\n      <td>17</td>\n      <td>女</td>\n      <td>4.48</td>\n      <td>9.09</td>\n      <td>180.0</td>\n      <td>10</td>\n      <td>46</td>\n      <td>2962</td>\n      <td>162.0</td>\n      <td>55.3</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>593 rows × 11 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 60
    }
   ],
   "source": [
    "# chengji_girl = pd.read_excel('D:\\数据分析\\第五阶段python\\模块四\\作业\\\\18级高一体测成绩汇总.xls',sheet_name=1,header=0)\n",
    "chengji_girl = pd.read_excel('./18级高一体测成绩汇总.xls',sheet_name=1,header=0)\n",
    "chengji_girl\n",
    "# chengji_girl.info()\n"
   ]
  },
  {
   "source": [
    "3、评分标准加载，pd.read_excel('./体侧成绩评分表.xls',header = [0,1])，header=[0,1]表示多层列索引"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    男肺活量       女肺活量      男50米跑      女50米跑       男体前屈       ...  女跳远       \\\n",
       "      成绩   分数    成绩   分数    成绩   分数    成绩   分数    成绩   分数  ...   成绩   分数   \n",
       "0   4540  100  3150  100   7.1  100   7.8  100  23.6  100  ...  204  100   \n",
       "1   4420   95  3100   95   7.2   95   7.9   95  21.5   95  ...  198   95   \n",
       "2   4300   90  3050   90   7.3   90   8.0   90  19.4   90  ...  192   90   \n",
       "3   4050   85  2900   85   7.4   85   8.3   85  17.2   85  ...  185   85   \n",
       "4   3800   80  2750   80   7.5   80   8.6   80  15.0   80  ...  178   80   \n",
       "5   3680   78  2650   78   7.7   78   8.8   78  13.6   78  ...  175   78   \n",
       "6   3560   76  2550   76   7.9   76   9.0   76  12.2   76  ...  172   76   \n",
       "7   3440   74  2450   74   8.1   74   9.2   74  10.8   74  ...  169   74   \n",
       "8   3320   72  2350   72   8.3   72   9.4   72   9.4   72  ...  166   72   \n",
       "9   3200   70  2250   70   8.5   70   9.6   70   8.0   70  ...  163   70   \n",
       "10  3080   68  2150   68   8.7   68   9.8   68   6.6   68  ...  160   68   \n",
       "11  2960   66  2050   66   8.9   66  10.0   66   5.2   66  ...  157   66   \n",
       "12  2840   64  1950   64   9.1   64  10.2   64   3.8   64  ...  154   64   \n",
       "13  2720   62  1850   62   9.3   62  10.4   62   2.4   62  ...  151   62   \n",
       "14  2600   60  1750   60   9.5   60  10.6   60   1.0   60  ...  148   60   \n",
       "15  2470   50  1710   50   9.7   50  10.8   50   0.0   50  ...  143   50   \n",
       "16  2340   40  1670   40   9.9   40  11.0   40  -1.0   40  ...  138   40   \n",
       "17  2210   30  1630   30  10.1   30  11.2   30  -2.0   30  ...  133   30   \n",
       "18  2080   20  1590   20  10.3   20  11.4   20  -3.0   20  ...  128   20   \n",
       "19  1950   10  1550   10  10.5   10  11.6   10  -4.0   10  ...  123   10   \n",
       "\n",
       "     男引体      女仰卧      男1000米跑      女800米跑       \n",
       "      成绩   分数  成绩   分数      成绩   分数     成绩   分数  \n",
       "0   16.0  100  53  100   3'30\"  100  3'24\"  100  \n",
       "1   15.0   95  51   95   3'35\"   95  3'30\"   95  \n",
       "2   14.0   90  49   90   3'40\"   90  3'36\"   90  \n",
       "3   13.0   85  46   85   3'47\"   85  3'43\"   85  \n",
       "4   12.0   80  43   80   3'55\"   80  3'50\"   80  \n",
       "5    NaN   78  41   78   4'00\"   78  3'55\"   78  \n",
       "6   11.0   76  39   76   4'05\"   76  4'00\"   76  \n",
       "7    NaN   74  37   74   4'10\"   74  4'05\"   74  \n",
       "8   10.0   72  35   72   4'15\"   72  4'10\"   72  \n",
       "9    NaN   70  33   70   4'20\"   70  4'15\"   70  \n",
       "10   9.0   68  31   68   4'25\"   68  4'20\"   68  \n",
       "11   NaN   66  29   66   4'30\"   66  4'25\"   66  \n",
       "12   8.0   64  27   64   4'35\"   64  4'30\"   64  \n",
       "13   NaN   62  25   62   4'40\"   62  4'35\"   62  \n",
       "14   7.0   60  23   60   4'45\"   60  4'40\"   60  \n",
       "15   6.0   50  21   50   5'05\"   50  4'50\"   50  \n",
       "16   5.0   40  19   40   5'25\"   40  5'00\"   40  \n",
       "17   4.0   30  17   30   5'45\"   30  5'10\"   30  \n",
       "18   3.0   20  15   20   6'05\"   20  5'20\"   20  \n",
       "19   2.0   10  13   10   6'25\"   10  5'30\"   10  \n",
       "\n",
       "[20 rows x 24 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"2\" halign=\"left\">男肺活量</th>\n      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n      <th colspan=\"2\" halign=\"left\">男体前屈</th>\n      <th>...</th>\n      <th colspan=\"2\" halign=\"left\">女跳远</th>\n      <th colspan=\"2\" halign=\"left\">男引体</th>\n      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n      <th colspan=\"2\" halign=\"left\">男1000米跑</th>\n      <th colspan=\"2\" halign=\"left\">女800米跑</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>...</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4540</td>\n      <td>100</td>\n      <td>3150</td>\n      <td>100</td>\n      <td>7.1</td>\n      <td>100</td>\n      <td>7.8</td>\n      <td>100</td>\n      <td>23.6</td>\n      <td>100</td>\n      <td>...</td>\n      <td>204</td>\n      <td>100</td>\n      <td>16.0</td>\n      <td>100</td>\n      <td>53</td>\n      <td>100</td>\n      <td>3'30\"</td>\n      <td>100</td>\n      <td>3'24\"</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4420</td>\n      <td>95</td>\n      <td>3100</td>\n      <td>95</td>\n      <td>7.2</td>\n      <td>95</td>\n      <td>7.9</td>\n      <td>95</td>\n      <td>21.5</td>\n      <td>95</td>\n      <td>...</td>\n      <td>198</td>\n      <td>95</td>\n      <td>15.0</td>\n      <td>95</td>\n      <td>51</td>\n      <td>95</td>\n      <td>3'35\"</td>\n      <td>95</td>\n      <td>3'30\"</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4300</td>\n      <td>90</td>\n      <td>3050</td>\n      <td>90</td>\n      <td>7.3</td>\n      <td>90</td>\n      <td>8.0</td>\n      <td>90</td>\n      <td>19.4</td>\n      <td>90</td>\n      <td>...</td>\n      <td>192</td>\n      <td>90</td>\n      <td>14.0</td>\n      <td>90</td>\n      <td>49</td>\n      <td>90</td>\n      <td>3'40\"</td>\n      <td>90</td>\n      <td>3'36\"</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4050</td>\n      <td>85</td>\n      <td>2900</td>\n      <td>85</td>\n      <td>7.4</td>\n      <td>85</td>\n      <td>8.3</td>\n      <td>85</td>\n      <td>17.2</td>\n      <td>85</td>\n      <td>...</td>\n      <td>185</td>\n      <td>85</td>\n      <td>13.0</td>\n      <td>85</td>\n      <td>46</td>\n      <td>85</td>\n      <td>3'47\"</td>\n      <td>85</td>\n      <td>3'43\"</td>\n      <td>85</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3800</td>\n      <td>80</td>\n      <td>2750</td>\n      <td>80</td>\n      <td>7.5</td>\n      <td>80</td>\n      <td>8.6</td>\n      <td>80</td>\n      <td>15.0</td>\n      <td>80</td>\n      <td>...</td>\n      <td>178</td>\n      <td>80</td>\n      <td>12.0</td>\n      <td>80</td>\n      <td>43</td>\n      <td>80</td>\n      <td>3'55\"</td>\n      <td>80</td>\n      <td>3'50\"</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>3680</td>\n      <td>78</td>\n      <td>2650</td>\n      <td>78</td>\n      <td>7.7</td>\n      <td>78</td>\n      <td>8.8</td>\n      <td>78</td>\n      <td>13.6</td>\n      <td>78</td>\n      <td>...</td>\n      <td>175</td>\n      <td>78</td>\n      <td>NaN</td>\n      <td>78</td>\n      <td>41</td>\n      <td>78</td>\n      <td>4'00\"</td>\n      <td>78</td>\n      <td>3'55\"</td>\n      <td>78</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>3560</td>\n      <td>76</td>\n      <td>2550</td>\n      <td>76</td>\n      <td>7.9</td>\n      <td>76</td>\n      <td>9.0</td>\n      <td>76</td>\n      <td>12.2</td>\n      <td>76</td>\n      <td>...</td>\n      <td>172</td>\n      <td>76</td>\n      <td>11.0</td>\n      <td>76</td>\n      <td>39</td>\n      <td>76</td>\n      <td>4'05\"</td>\n      <td>76</td>\n      <td>4'00\"</td>\n      <td>76</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>3440</td>\n      <td>74</td>\n      <td>2450</td>\n      <td>74</td>\n      <td>8.1</td>\n      <td>74</td>\n      <td>9.2</td>\n      <td>74</td>\n      <td>10.8</td>\n      <td>74</td>\n      <td>...</td>\n      <td>169</td>\n      <td>74</td>\n      <td>NaN</td>\n      <td>74</td>\n      <td>37</td>\n      <td>74</td>\n      <td>4'10\"</td>\n      <td>74</td>\n      <td>4'05\"</td>\n      <td>74</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>3320</td>\n      <td>72</td>\n      <td>2350</td>\n      <td>72</td>\n      <td>8.3</td>\n      <td>72</td>\n      <td>9.4</td>\n      <td>72</td>\n      <td>9.4</td>\n      <td>72</td>\n      <td>...</td>\n      <td>166</td>\n      <td>72</td>\n      <td>10.0</td>\n      <td>72</td>\n      <td>35</td>\n      <td>72</td>\n      <td>4'15\"</td>\n      <td>72</td>\n      <td>4'10\"</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>3200</td>\n      <td>70</td>\n      <td>2250</td>\n      <td>70</td>\n      <td>8.5</td>\n      <td>70</td>\n      <td>9.6</td>\n      <td>70</td>\n      <td>8.0</td>\n      <td>70</td>\n      <td>...</td>\n      <td>163</td>\n      <td>70</td>\n      <td>NaN</td>\n      <td>70</td>\n      <td>33</td>\n      <td>70</td>\n      <td>4'20\"</td>\n      <td>70</td>\n      <td>4'15\"</td>\n      <td>70</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>3080</td>\n      <td>68</td>\n      <td>2150</td>\n      <td>68</td>\n      <td>8.7</td>\n      <td>68</td>\n      <td>9.8</td>\n      <td>68</td>\n      <td>6.6</td>\n      <td>68</td>\n      <td>...</td>\n      <td>160</td>\n      <td>68</td>\n      <td>9.0</td>\n      <td>68</td>\n      <td>31</td>\n      <td>68</td>\n      <td>4'25\"</td>\n      <td>68</td>\n      <td>4'20\"</td>\n      <td>68</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2960</td>\n      <td>66</td>\n      <td>2050</td>\n      <td>66</td>\n      <td>8.9</td>\n      <td>66</td>\n      <td>10.0</td>\n      <td>66</td>\n      <td>5.2</td>\n      <td>66</td>\n      <td>...</td>\n      <td>157</td>\n      <td>66</td>\n      <td>NaN</td>\n      <td>66</td>\n      <td>29</td>\n      <td>66</td>\n      <td>4'30\"</td>\n      <td>66</td>\n      <td>4'25\"</td>\n      <td>66</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2840</td>\n      <td>64</td>\n      <td>1950</td>\n      <td>64</td>\n      <td>9.1</td>\n      <td>64</td>\n      <td>10.2</td>\n      <td>64</td>\n      <td>3.8</td>\n      <td>64</td>\n      <td>...</td>\n      <td>154</td>\n      <td>64</td>\n      <td>8.0</td>\n      <td>64</td>\n      <td>27</td>\n      <td>64</td>\n      <td>4'35\"</td>\n      <td>64</td>\n      <td>4'30\"</td>\n      <td>64</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2720</td>\n      <td>62</td>\n      <td>1850</td>\n      <td>62</td>\n      <td>9.3</td>\n      <td>62</td>\n      <td>10.4</td>\n      <td>62</td>\n      <td>2.4</td>\n      <td>62</td>\n      <td>...</td>\n      <td>151</td>\n      <td>62</td>\n      <td>NaN</td>\n      <td>62</td>\n      <td>25</td>\n      <td>62</td>\n      <td>4'40\"</td>\n      <td>62</td>\n      <td>4'35\"</td>\n      <td>62</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2600</td>\n      <td>60</td>\n      <td>1750</td>\n      <td>60</td>\n      <td>9.5</td>\n      <td>60</td>\n      <td>10.6</td>\n      <td>60</td>\n      <td>1.0</td>\n      <td>60</td>\n      <td>...</td>\n      <td>148</td>\n      <td>60</td>\n      <td>7.0</td>\n      <td>60</td>\n      <td>23</td>\n      <td>60</td>\n      <td>4'45\"</td>\n      <td>60</td>\n      <td>4'40\"</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2470</td>\n      <td>50</td>\n      <td>1710</td>\n      <td>50</td>\n      <td>9.7</td>\n      <td>50</td>\n      <td>10.8</td>\n      <td>50</td>\n      <td>0.0</td>\n      <td>50</td>\n      <td>...</td>\n      <td>143</td>\n      <td>50</td>\n      <td>6.0</td>\n      <td>50</td>\n      <td>21</td>\n      <td>50</td>\n      <td>5'05\"</td>\n      <td>50</td>\n      <td>4'50\"</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2340</td>\n      <td>40</td>\n      <td>1670</td>\n      <td>40</td>\n      <td>9.9</td>\n      <td>40</td>\n      <td>11.0</td>\n      <td>40</td>\n      <td>-1.0</td>\n      <td>40</td>\n      <td>...</td>\n      <td>138</td>\n      <td>40</td>\n      <td>5.0</td>\n      <td>40</td>\n      <td>19</td>\n      <td>40</td>\n      <td>5'25\"</td>\n      <td>40</td>\n      <td>5'00\"</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2210</td>\n      <td>30</td>\n      <td>1630</td>\n      <td>30</td>\n      <td>10.1</td>\n      <td>30</td>\n      <td>11.2</td>\n      <td>30</td>\n      <td>-2.0</td>\n      <td>30</td>\n      <td>...</td>\n      <td>133</td>\n      <td>30</td>\n      <td>4.0</td>\n      <td>30</td>\n      <td>17</td>\n      <td>30</td>\n      <td>5'45\"</td>\n      <td>30</td>\n      <td>5'10\"</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2080</td>\n      <td>20</td>\n      <td>1590</td>\n      <td>20</td>\n      <td>10.3</td>\n      <td>20</td>\n      <td>11.4</td>\n      <td>20</td>\n      <td>-3.0</td>\n      <td>20</td>\n      <td>...</td>\n      <td>128</td>\n      <td>20</td>\n      <td>3.0</td>\n      <td>20</td>\n      <td>15</td>\n      <td>20</td>\n      <td>6'05\"</td>\n      <td>20</td>\n      <td>5'20\"</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>1950</td>\n      <td>10</td>\n      <td>1550</td>\n      <td>10</td>\n      <td>10.5</td>\n      <td>10</td>\n      <td>11.6</td>\n      <td>10</td>\n      <td>-4.0</td>\n      <td>10</td>\n      <td>...</td>\n      <td>123</td>\n      <td>10</td>\n      <td>2.0</td>\n      <td>10</td>\n      <td>13</td>\n      <td>10</td>\n      <td>6'25\"</td>\n      <td>10</td>\n      <td>5'30\"</td>\n      <td>10</td>\n    </tr>\n  </tbody>\n</table>\n<p>20 rows × 24 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 61
    }
   ],
   "source": [
    "pingfen = pd.read_excel('./体侧成绩评分表.xls',sheet_name=0,header=[0,1])\n",
    "pingfen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "source": [
    "4、数据类型转换\n",
    "\n",
    "男1000米跑，数据类型是str，并且是4’26这种形式，需要变成float类型的值\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     班级 性别  男1000米跑  男50米跑    男跳远  男体前屈  男引体  男肺活量     身高    体重  BMI\n",
       "0     1  男     4.13   8.88  195.0    12    1  2785  170.0  72.6    0\n",
       "1     1  男     4.16   7.70  225.0    11    7  3133  174.0  52.7    0\n",
       "2     1  男     4.09   8.45  218.0    14    1  3901  169.0  46.5    0\n",
       "3     1  男     4.21   8.05  206.0    13    1  4946  183.0  79.7    0\n",
       "4     1  男     3.44   7.52  210.0    13    9  3538  171.0  54.7    0\n",
       "..   .. ..      ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "472  17  男     4.23   8.27  208.0    10    0  4647  176.0  69.5    0\n",
       "473  17  男     5.19   9.55  210.0    15    6  7042  177.0  76.0    0\n",
       "474  17  男     3.25   7.50  252.0    13   13  5755  181.0  65.0    0\n",
       "475  17  男     4.39   7.81  208.0    14   11  5688  172.0  51.7    0\n",
       "476  17  男     0.00   0.00    0.0     0    0     0    0.0   0.0    0\n",
       "\n",
       "[477 rows x 11 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>班级</th>\n      <th>性别</th>\n      <th>男1000米跑</th>\n      <th>男50米跑</th>\n      <th>男跳远</th>\n      <th>男体前屈</th>\n      <th>男引体</th>\n      <th>男肺活量</th>\n      <th>身高</th>\n      <th>体重</th>\n      <th>BMI</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4.13</td>\n      <td>8.88</td>\n      <td>195.0</td>\n      <td>12</td>\n      <td>1</td>\n      <td>2785</td>\n      <td>170.0</td>\n      <td>72.6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4.16</td>\n      <td>7.70</td>\n      <td>225.0</td>\n      <td>11</td>\n      <td>7</td>\n      <td>3133</td>\n      <td>174.0</td>\n      <td>52.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4.09</td>\n      <td>8.45</td>\n      <td>218.0</td>\n      <td>14</td>\n      <td>1</td>\n      <td>3901</td>\n      <td>169.0</td>\n      <td>46.5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4.21</td>\n      <td>8.05</td>\n      <td>206.0</td>\n      <td>13</td>\n      <td>1</td>\n      <td>4946</td>\n      <td>183.0</td>\n      <td>79.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>男</td>\n      <td>3.44</td>\n      <td>7.52</td>\n      <td>210.0</td>\n      <td>13</td>\n      <td>9</td>\n      <td>3538</td>\n      <td>171.0</td>\n      <td>54.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>472</th>\n      <td>17</td>\n      <td>男</td>\n      <td>4.23</td>\n      <td>8.27</td>\n      <td>208.0</td>\n      <td>10</td>\n      <td>0</td>\n      <td>4647</td>\n      <td>176.0</td>\n      <td>69.5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>473</th>\n      <td>17</td>\n      <td>男</td>\n      <td>5.19</td>\n      <td>9.55</td>\n      <td>210.0</td>\n      <td>15</td>\n      <td>6</td>\n      <td>7042</td>\n      <td>177.0</td>\n      <td>76.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>474</th>\n      <td>17</td>\n      <td>男</td>\n      <td>3.25</td>\n      <td>7.50</td>\n      <td>252.0</td>\n      <td>13</td>\n      <td>13</td>\n      <td>5755</td>\n      <td>181.0</td>\n      <td>65.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>475</th>\n      <td>17</td>\n      <td>男</td>\n      <td>4.39</td>\n      <td>7.81</td>\n      <td>208.0</td>\n      <td>14</td>\n      <td>11</td>\n      <td>5688</td>\n      <td>172.0</td>\n      <td>51.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>476</th>\n      <td>17</td>\n      <td>男</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>477 rows × 11 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 62
    }
   ],
   "source": [
    "chengji_boy['男1000米跑'] = chengji_boy['男1000米跑'].map( lambda x:float(str(x).replace(\"'\",'.')))\n",
    "# chengji_boy.info()\n",
    "chengji_boy"
   ]
  },
  {
   "source": [
    "评分标准中男1000米跑和女800米跑的成绩都是4‘10’‘这种形式，需要转化为float类型值"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   男肺活量       女肺活量      男50米跑      女50米跑       男体前屈       ...  女跳远        男引体  \\\n",
       "     成绩   分数    成绩   分数    成绩   分数    成绩   分数    成绩   分数  ...   成绩   分数    成绩   \n",
       "0  4540  100  3150  100   7.1  100   7.8  100  23.6  100  ...  204  100  16.0   \n",
       "1  4420   95  3100   95   7.2   95   7.9   95  21.5   95  ...  198   95  15.0   \n",
       "2  4300   90  3050   90   7.3   90   8.0   90  19.4   90  ...  192   90  14.0   \n",
       "3  4050   85  2900   85   7.4   85   8.3   85  17.2   85  ...  185   85  13.0   \n",
       "4  3800   80  2750   80   7.5   80   8.6   80  15.0   80  ...  178   80  12.0   \n",
       "5  3680   78  2650   78   7.7   78   8.8   78  13.6   78  ...  175   78   NaN   \n",
       "\n",
       "       女仰卧      男1000米跑      女800米跑       \n",
       "    分数  成绩   分数      成绩   分数     成绩   分数  \n",
       "0  100  53  100    3.30  100   3.24  100  \n",
       "1   95  51   95    3.35   95   3.30   95  \n",
       "2   90  49   90    3.40   90   3.36   90  \n",
       "3   85  46   85    3.47   85   3.43   85  \n",
       "4   80  43   80    3.55   80   3.50   80  \n",
       "5   78  41   78    4.00   78   3.55   78  \n",
       "\n",
       "[6 rows x 24 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"2\" halign=\"left\">男肺活量</th>\n      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n      <th colspan=\"2\" halign=\"left\">男体前屈</th>\n      <th>...</th>\n      <th colspan=\"2\" halign=\"left\">女跳远</th>\n      <th colspan=\"2\" halign=\"left\">男引体</th>\n      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n      <th colspan=\"2\" halign=\"left\">男1000米跑</th>\n      <th colspan=\"2\" halign=\"left\">女800米跑</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>...</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n      <th>成绩</th>\n      <th>分数</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4540</td>\n      <td>100</td>\n      <td>3150</td>\n      <td>100</td>\n      <td>7.1</td>\n      <td>100</td>\n      <td>7.8</td>\n      <td>100</td>\n      <td>23.6</td>\n      <td>100</td>\n      <td>...</td>\n      <td>204</td>\n      <td>100</td>\n      <td>16.0</td>\n      <td>100</td>\n      <td>53</td>\n      <td>100</td>\n      <td>3.30</td>\n      <td>100</td>\n      <td>3.24</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4420</td>\n      <td>95</td>\n      <td>3100</td>\n      <td>95</td>\n      <td>7.2</td>\n      <td>95</td>\n      <td>7.9</td>\n      <td>95</td>\n      <td>21.5</td>\n      <td>95</td>\n      <td>...</td>\n      <td>198</td>\n      <td>95</td>\n      <td>15.0</td>\n      <td>95</td>\n      <td>51</td>\n      <td>95</td>\n      <td>3.35</td>\n      <td>95</td>\n      <td>3.30</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4300</td>\n      <td>90</td>\n      <td>3050</td>\n      <td>90</td>\n      <td>7.3</td>\n      <td>90</td>\n      <td>8.0</td>\n      <td>90</td>\n      <td>19.4</td>\n      <td>90</td>\n      <td>...</td>\n      <td>192</td>\n      <td>90</td>\n      <td>14.0</td>\n      <td>90</td>\n      <td>49</td>\n      <td>90</td>\n      <td>3.40</td>\n      <td>90</td>\n      <td>3.36</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4050</td>\n      <td>85</td>\n      <td>2900</td>\n      <td>85</td>\n      <td>7.4</td>\n      <td>85</td>\n      <td>8.3</td>\n      <td>85</td>\n      <td>17.2</td>\n      <td>85</td>\n      <td>...</td>\n      <td>185</td>\n      <td>85</td>\n      <td>13.0</td>\n      <td>85</td>\n      <td>46</td>\n      <td>85</td>\n      <td>3.47</td>\n      <td>85</td>\n      <td>3.43</td>\n      <td>85</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3800</td>\n      <td>80</td>\n      <td>2750</td>\n      <td>80</td>\n      <td>7.5</td>\n      <td>80</td>\n      <td>8.6</td>\n      <td>80</td>\n      <td>15.0</td>\n      <td>80</td>\n      <td>...</td>\n      <td>178</td>\n      <td>80</td>\n      <td>12.0</td>\n      <td>80</td>\n      <td>43</td>\n      <td>80</td>\n      <td>3.55</td>\n      <td>80</td>\n      <td>3.50</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>3680</td>\n      <td>78</td>\n      <td>2650</td>\n      <td>78</td>\n      <td>7.7</td>\n      <td>78</td>\n      <td>8.8</td>\n      <td>78</td>\n      <td>13.6</td>\n      <td>78</td>\n      <td>...</td>\n      <td>175</td>\n      <td>78</td>\n      <td>NaN</td>\n      <td>78</td>\n      <td>41</td>\n      <td>78</td>\n      <td>4.00</td>\n      <td>78</td>\n      <td>3.55</td>\n      <td>78</td>\n    </tr>\n  </tbody>\n</table>\n<p>6 rows × 24 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 63
    }
   ],
   "source": [
    "pingfen[('男1000米跑','成绩')]=pingfen[('男1000米跑','成绩')].map(lambda x:float(str(x).replace('\"',' ').replace(\"'\",'.')))\n",
    "pingfen[('女800米跑','成绩')]=pingfen[('女800米跑','成绩')].map(lambda x:float(str(x).replace('\"',' ').replace(\"'\",'.')))\n",
    "pingfen[:6]"
   ]
  },
  {
   "source": [
    "其他所有数值类型的值，都要转换为float类型的值"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 477 entries, 0 to 476\n",
      "Data columns (total 11 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   班级       477 non-null    int64  \n",
      " 1   性别       477 non-null    object \n",
      " 2   男1000米跑  477 non-null    float64\n",
      " 3   男50米跑    477 non-null    float64\n",
      " 4   男跳远      477 non-null    float64\n",
      " 5   男体前屈     477 non-null    int64  \n",
      " 6   男引体      477 non-null    int64  \n",
      " 7   男肺活量     477 non-null    int64  \n",
      " 8   身高       477 non-null    float64\n",
      " 9   体重       477 non-null    float64\n",
      " 10  BMI      477 non-null    int64  \n",
      "dtypes: float64(5), int64(5), object(1)\n",
      "memory usage: 41.1+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 593 entries, 0 to 592\n",
      "Data columns (total 11 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   班级      593 non-null    int64  \n",
      " 1   性别      593 non-null    object \n",
      " 2   女800米跑  593 non-null    float64\n",
      " 3   女50米跑   593 non-null    float64\n",
      " 4   女跳远     593 non-null    float64\n",
      " 5   女体前屈    593 non-null    int64  \n",
      " 6   女仰卧     593 non-null    int64  \n",
      " 7   女肺活量    593 non-null    int64  \n",
      " 8   身高      593 non-null    float64\n",
      " 9   体重      593 non-null    float64\n",
      " 10  BMI     593 non-null    int64  \n",
      "dtypes: float64(5), int64(5), object(1)\n",
      "memory usage: 51.1+ KB\n"
     ]
    }
   ],
   "source": [
    "chengji_boy.info()\n",
    "chengji_girl.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 477 entries, 0 to 476\nData columns (total 11 columns):\n #   Column   Non-Null Count  Dtype  \n---  ------   --------------  -----  \n 0   班级       477 non-null    int64  \n 1   性别       477 non-null    object \n 2   男1000米跑  477 non-null    float64\n 3   男50米跑    477 non-null    float64\n 4   男跳远      477 non-null    float64\n 5   男体前屈     477 non-null    float64\n 6   男引体      477 non-null    float64\n 7   男肺活量     477 non-null    float64\n 8   身高       477 non-null    float64\n 9   体重       477 non-null    float64\n 10  BMI      477 non-null    float64\ndtypes: float64(9), int64(1), object(1)\nmemory usage: 41.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#男体前屈  男引体 男肺活量 BMI\n",
    "#女体前屈 女仰卧 女肺活量 BMI   \n",
    "#通过查看发现这几个指标不是float类型  \n",
    "chengji_boy[['男体前屈','男引体','男肺活量','BMI']] = chengji_boy[['男体前屈','男引体','男肺活量','BMI']].astype(float)\n",
    "chengji_boy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 593 entries, 0 to 592\nData columns (total 11 columns):\n #   Column  Non-Null Count  Dtype  \n---  ------  --------------  -----  \n 0   班级      593 non-null    int64  \n 1   性别      593 non-null    object \n 2   女800米跑  593 non-null    float64\n 3   女50米跑   593 non-null    float64\n 4   女跳远     593 non-null    float64\n 5   女体前屈    593 non-null    float64\n 6   女仰卧     593 non-null    float64\n 7   女肺活量    593 non-null    float64\n 8   身高      593 non-null    float64\n 9   体重      593 non-null    float64\n 10  BMI     593 non-null    float64\ndtypes: float64(9), int64(1), object(1)\nmemory usage: 51.1+ KB\n"
     ]
    }
   ],
   "source": [
    "chengji_girl[['女体前屈','女仰卧','女肺活量','BMI']] = chengji_girl[['女体前屈','女仰卧','女肺活量','BMI']].astype(float)\n",
    "chengji_girl.info()"
   ]
  },
  {
   "source": [
    "5、对体测成绩进行分数转换，跑步类（越小越好）；跳远、体前屈（越大越好）\n",
    "\n",
    " 使用map、apply、transform方法\n",
    "\n",
    " 列索引重排\n",
    "\n",
    " 转换之后效果\n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#男生\n",
    "# ('班级','性别','男1000米跑','男1000米跑分数','男50米跑','男50米跑分数','男跳远','男跳远分数','男体前屈','男体前屈分数','男引体','男引体分数','男肺活量','男肺活量分数','身高','体重','BMI')\n",
    "chengji_boy['男肺活量分数'] = chengji_boy['男肺活量'].map(lambda x:pingfen['男肺活量']['分数'][(x>=pingfen['男肺活量']['成绩'])].max())\n",
    "\n",
    "chengji_boy['男引体分数'] = chengji_boy['男引体'].map(lambda x:pingfen['男引体']['分数'][(x>=pingfen['男引体']['成绩'])].max())\n",
    "\n",
    "chengji_boy['男体前屈分数'] = chengji_boy['男体前屈'].map(lambda x:pingfen['男体前屈']['分数'][(x>=pingfen['男体前屈']['成绩'])].max())\n",
    "\n",
    "chengji_boy['男跳远分数'] = chengji_boy['男跳远'].map(lambda x:pingfen['男跳远']['分数'][(x>=pingfen['男跳远']['成绩'])].max())\n",
    "\n",
    "chengji_boy['男50米跑分数'] = chengji_boy['男50米跑'].map(lambda x:pingfen['男50米跑']['分数'][(x<=pingfen['男50米跑']['成绩'])].max())\n",
    "\n",
    "chengji_boy['男1000米跑分数'] = chengji_boy['男1000米跑'].map(lambda x:pingfen['男1000米跑']['分数'][(x<=pingfen['男1000米跑']['成绩'])].max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数    男跳远  男跳远分数  男体前屈  男体前屈分数  \\\n",
       "0     1  男     4.13         72   8.88       66  195.0   60.0  12.0    74.0   \n",
       "1     1  男     4.16         70   7.70       78  225.0   74.0  11.0    74.0   \n",
       "2     1  男     4.09         74   8.45       70  218.0   70.0  14.0    78.0   \n",
       "3     1  男     4.21         68   8.05       74  206.0   64.0  13.0    76.0   \n",
       "4     1  男     3.44         85   7.52       78  210.0   66.0  13.0    76.0   \n",
       "..   .. ..      ...        ...    ...      ...    ...    ...   ...     ...   \n",
       "472  17  男     4.23         68   8.27       72  208.0   66.0  10.0    72.0   \n",
       "473  17  男     5.19         40   9.55       50  210.0   66.0  15.0    80.0   \n",
       "474  17  男     3.25        100   7.50       80  252.0   90.0  13.0    76.0   \n",
       "475  17  男     4.39         62   7.81       76  208.0   66.0  14.0    78.0   \n",
       "476  17  男     0.00        100   0.00      100    0.0    0.0   0.0    50.0   \n",
       "\n",
       "      男引体  男引体分数    男肺活量  男肺活量分数     身高    体重  BMI  \n",
       "0     1.0    0.0  2785.0    62.0  170.0  72.6  0.0  \n",
       "1     7.0   60.0  3133.0    68.0  174.0  52.7  0.0  \n",
       "2     1.0    0.0  3901.0    80.0  169.0  46.5  0.0  \n",
       "3     1.0    0.0  4946.0   100.0  183.0  79.7  0.0  \n",
       "4     9.0   68.0  3538.0    74.0  171.0  54.7  0.0  \n",
       "..    ...    ...     ...     ...    ...   ...  ...  \n",
       "472   0.0    0.0  4647.0   100.0  176.0  69.5  0.0  \n",
       "473   6.0   50.0  7042.0   100.0  177.0  76.0  0.0  \n",
       "474  13.0   85.0  5755.0   100.0  181.0  65.0  0.0  \n",
       "475  11.0   76.0  5688.0   100.0  172.0  51.7  0.0  \n",
       "476   0.0    0.0     0.0     0.0    0.0   0.0  0.0  \n",
       "\n",
       "[477 rows x 17 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>班级</th>\n      <th>性别</th>\n      <th>男1000米跑</th>\n      <th>男1000米跑分数</th>\n      <th>男50米跑</th>\n      <th>男50米跑分数</th>\n      <th>男跳远</th>\n      <th>男跳远分数</th>\n      <th>男体前屈</th>\n      <th>男体前屈分数</th>\n      <th>男引体</th>\n      <th>男引体分数</th>\n      <th>男肺活量</th>\n      <th>男肺活量分数</th>\n      <th>身高</th>\n      <th>体重</th>\n      <th>BMI</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4.13</td>\n      <td>72</td>\n      <td>8.88</td>\n      <td>66</td>\n      <td>195.0</td>\n      <td>60.0</td>\n      <td>12.0</td>\n      <td>74.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2785.0</td>\n      <td>62.0</td>\n      <td>170.0</td>\n      <td>72.6</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4.16</td>\n      <td>70</td>\n      <td>7.70</td>\n      <td>78</td>\n      <td>225.0</td>\n      <td>74.0</td>\n      <td>11.0</td>\n      <td>74.0</td>\n      <td>7.0</td>\n      <td>60.0</td>\n      <td>3133.0</td>\n      <td>68.0</td>\n      <td>174.0</td>\n      <td>52.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4.09</td>\n      <td>74</td>\n      <td>8.45</td>\n      <td>70</td>\n      <td>218.0</td>\n      <td>70.0</td>\n      <td>14.0</td>\n      <td>78.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>3901.0</td>\n      <td>80.0</td>\n      <td>169.0</td>\n      <td>46.5</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>男</td>\n      <td>4.21</td>\n      <td>68</td>\n      <td>8.05</td>\n      <td>74</td>\n      <td>206.0</td>\n      <td>64.0</td>\n      <td>13.0</td>\n      <td>76.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4946.0</td>\n      <td>100.0</td>\n      <td>183.0</td>\n      <td>79.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>男</td>\n      <td>3.44</td>\n      <td>85</td>\n      <td>7.52</td>\n      <td>78</td>\n      <td>210.0</td>\n      <td>66.0</td>\n      <td>13.0</td>\n      <td>76.0</td>\n      <td>9.0</td>\n      <td>68.0</td>\n      <td>3538.0</td>\n      <td>74.0</td>\n      <td>171.0</td>\n      <td>54.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>472</th>\n      <td>17</td>\n      <td>男</td>\n      <td>4.23</td>\n      <td>68</td>\n      <td>8.27</td>\n      <td>72</td>\n      <td>208.0</td>\n      <td>66.0</td>\n      <td>10.0</td>\n      <td>72.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4647.0</td>\n      <td>100.0</td>\n      <td>176.0</td>\n      <td>69.5</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>473</th>\n      <td>17</td>\n      <td>男</td>\n      <td>5.19</td>\n      <td>40</td>\n      <td>9.55</td>\n      <td>50</td>\n      <td>210.0</td>\n      <td>66.0</td>\n      <td>15.0</td>\n      <td>80.0</td>\n      <td>6.0</td>\n      <td>50.0</td>\n      <td>7042.0</td>\n      <td>100.0</td>\n      <td>177.0</td>\n      <td>76.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>474</th>\n      <td>17</td>\n      <td>男</td>\n      <td>3.25</td>\n      <td>100</td>\n      <td>7.50</td>\n      <td>80</td>\n      <td>252.0</td>\n      <td>90.0</td>\n      <td>13.0</td>\n      <td>76.0</td>\n      <td>13.0</td>\n      <td>85.0</td>\n      <td>5755.0</td>\n      <td>100.0</td>\n      <td>181.0</td>\n      <td>65.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>475</th>\n      <td>17</td>\n      <td>男</td>\n      <td>4.39</td>\n      <td>62</td>\n      <td>7.81</td>\n      <td>76</td>\n      <td>208.0</td>\n      <td>66.0</td>\n      <td>14.0</td>\n      <td>78.0</td>\n      <td>11.0</td>\n      <td>76.0</td>\n      <td>5688.0</td>\n      <td>100.0</td>\n      <td>172.0</td>\n      <td>51.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>476</th>\n      <td>17</td>\n      <td>男</td>\n      <td>0.00</td>\n      <td>100</td>\n      <td>0.00</td>\n      <td>100</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>50.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>477 rows × 17 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 74
    }
   ],
   "source": [
    "list_name_boy = ['班级','性别','男1000米跑','男1000米跑分数','男50米跑','男50米跑分数','男跳远','男跳远分数','男体前屈','男体前屈分数','男引体','男引体分数','男肺活量','男肺活量分数','身高','体重','BMI']\n",
    "\n",
    "data_boy=chengji_boy[list_name_boy]\n",
    "\n",
    "data_boy.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#女生\n",
    "# ('班级','性别','女800米跑','女800米跑分数','女50米跑','女50米跑分数','女跳远','女跳远分数','女体前屈','女体前屈分数','女仰卧','女仰卧分数','女肺活量','女肺活量分数','身高','体重','BMI')\n",
    "\n",
    "chengji_girl['女肺活量分数'] = chengji_girl['女肺活量'].map(lambda x:pingfen['女肺活量']['分数'][(x>=pingfen['女肺活量']['成绩'])].max())\n",
    "\n",
    "chengji_girl['女仰卧分数'] = chengji_girl['女仰卧'].map(lambda x:pingfen['女仰卧']['分数'][(x>=pingfen['女仰卧']['成绩'])].max())\n",
    "\n",
    "chengji_girl['女体前屈分数'] = chengji_girl['女体前屈'].map(lambda x:pingfen['女体前屈']['分数'][(x>=pingfen['女体前屈']['成绩'])].max())\n",
    "\n",
    "chengji_girl['女跳远分数'] = chengji_girl['女跳远'].map(lambda x:pingfen['女跳远']['分数'][(x>=pingfen['女跳远']['成绩'])].max())\n",
    "\n",
    "chengji_girl['女50米跑分数'] = chengji_girl['女50米跑'].map(lambda x:pingfen['女50米跑']['分数'][(x<=pingfen['女50米跑']['成绩'])].max())\n",
    "\n",
    "chengji_girl['女800米跑分数'] = chengji_girl['女800米跑'].map(lambda x:pingfen['女800米跑']['分数'][(x<=pingfen['女800米跑']['成绩'])].max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数    女跳远  女跳远分数  女体前屈  女体前屈分数  \\\n",
       "0     1  女    3.22     100.0   9.32     72.0  185.0   85.0  16.0    76.0   \n",
       "1     1  女    4.59      40.0  11.44     10.0  148.0   60.0   9.0    66.0   \n",
       "2     1  女    3.46      80.0  13.40      0.0  150.0   60.0   7.0    64.0   \n",
       "3     1  女    3.39      85.0   9.52     70.0  172.0   76.0  21.0    90.0   \n",
       "4     1  女    3.43      85.0   9.79     68.0  145.0   50.0   8.0    64.0   \n",
       "..   .. ..     ...       ...    ...      ...    ...    ...   ...     ...   \n",
       "588  17  女    3.51      78.0   9.60     70.0  150.0   60.0  24.0    95.0   \n",
       "589  17  女    4.00      76.0  10.18     64.0  150.0   60.0  13.0    72.0   \n",
       "590  17  女    3.45      80.0  10.18     64.0  152.0   62.0  15.0    76.0   \n",
       "591  17  女    4.01      74.0   9.67     68.0  165.0   70.0  10.0    68.0   \n",
       "592  17  女    4.48      50.0   9.09     74.0  180.0   80.0  10.0    68.0   \n",
       "\n",
       "      女仰卧  女仰卧分数    女肺活量  女肺活量分数     身高    体重  BMI  \n",
       "0    48.0   85.0  3775.0   100.0  163.0  51.3  0.0  \n",
       "1    29.0   66.0  3683.0   100.0  163.0  66.6  0.0  \n",
       "2    40.0   76.0  3331.0   100.0  157.0  60.0  0.0  \n",
       "3    46.0   85.0  3701.0   100.0  160.0  50.7  0.0  \n",
       "4    34.0   70.0  3592.0   100.0  167.0  63.9  0.0  \n",
       "..    ...    ...     ...     ...    ...   ...  ...  \n",
       "588  41.0   78.0  2255.0    70.0  158.0  49.0  0.0  \n",
       "589  36.0   72.0  2937.0    85.0  161.0  55.7  0.0  \n",
       "590  35.0   72.0  2592.0    76.0  165.0  48.6  0.0  \n",
       "591  41.0   78.0  1829.0    60.0  154.0  43.6  0.0  \n",
       "592  46.0   85.0  2962.0    85.0  162.0  55.3  0.0  \n",
       "\n",
       "[593 rows x 17 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>班级</th>\n      <th>性别</th>\n      <th>女800米跑</th>\n      <th>女800米跑分数</th>\n      <th>女50米跑</th>\n      <th>女50米跑分数</th>\n      <th>女跳远</th>\n      <th>女跳远分数</th>\n      <th>女体前屈</th>\n      <th>女体前屈分数</th>\n      <th>女仰卧</th>\n      <th>女仰卧分数</th>\n      <th>女肺活量</th>\n      <th>女肺活量分数</th>\n      <th>身高</th>\n      <th>体重</th>\n      <th>BMI</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>女</td>\n      <td>3.22</td>\n      <td>100.0</td>\n      <td>9.32</td>\n      <td>72.0</td>\n      <td>185.0</td>\n      <td>85.0</td>\n      <td>16.0</td>\n      <td>76.0</td>\n      <td>48.0</td>\n      <td>85.0</td>\n      <td>3775.0</td>\n      <td>100.0</td>\n      <td>163.0</td>\n      <td>51.3</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>女</td>\n      <td>4.59</td>\n      <td>40.0</td>\n      <td>11.44</td>\n      <td>10.0</td>\n      <td>148.0</td>\n      <td>60.0</td>\n      <td>9.0</td>\n      <td>66.0</td>\n      <td>29.0</td>\n      <td>66.0</td>\n      <td>3683.0</td>\n      <td>100.0</td>\n      <td>163.0</td>\n      <td>66.6</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>女</td>\n      <td>3.46</td>\n      <td>80.0</td>\n      <td>13.40</td>\n      <td>0.0</td>\n      <td>150.0</td>\n      <td>60.0</td>\n      <td>7.0</td>\n      <td>64.0</td>\n      <td>40.0</td>\n      <td>76.0</td>\n      <td>3331.0</td>\n      <td>100.0</td>\n      <td>157.0</td>\n      <td>60.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>女</td>\n      <td>3.39</td>\n      <td>85.0</td>\n      <td>9.52</td>\n      <td>70.0</td>\n      <td>172.0</td>\n      <td>76.0</td>\n      <td>21.0</td>\n      <td>90.0</td>\n      <td>46.0</td>\n      <td>85.0</td>\n      <td>3701.0</td>\n      <td>100.0</td>\n      <td>160.0</td>\n      <td>50.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>女</td>\n      <td>3.43</td>\n      <td>85.0</td>\n      <td>9.79</td>\n      <td>68.0</td>\n      <td>145.0</td>\n      <td>50.0</td>\n      <td>8.0</td>\n      <td>64.0</td>\n      <td>34.0</td>\n      <td>70.0</td>\n      <td>3592.0</td>\n      <td>100.0</td>\n      <td>167.0</td>\n      <td>63.9</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>588</th>\n      <td>17</td>\n      <td>女</td>\n      <td>3.51</td>\n      <td>78.0</td>\n      <td>9.60</td>\n      <td>70.0</td>\n      <td>150.0</td>\n      <td>60.0</td>\n      <td>24.0</td>\n      <td>95.0</td>\n      <td>41.0</td>\n      <td>78.0</td>\n      <td>2255.0</td>\n      <td>70.0</td>\n      <td>158.0</td>\n      <td>49.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>589</th>\n      <td>17</td>\n      <td>女</td>\n      <td>4.00</td>\n      <td>76.0</td>\n      <td>10.18</td>\n      <td>64.0</td>\n      <td>150.0</td>\n      <td>60.0</td>\n      <td>13.0</td>\n      <td>72.0</td>\n      <td>36.0</td>\n      <td>72.0</td>\n      <td>2937.0</td>\n      <td>85.0</td>\n      <td>161.0</td>\n      <td>55.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>590</th>\n      <td>17</td>\n      <td>女</td>\n      <td>3.45</td>\n      <td>80.0</td>\n      <td>10.18</td>\n      <td>64.0</td>\n      <td>152.0</td>\n      <td>62.0</td>\n      <td>15.0</td>\n      <td>76.0</td>\n      <td>35.0</td>\n      <td>72.0</td>\n      <td>2592.0</td>\n      <td>76.0</td>\n      <td>165.0</td>\n      <td>48.6</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>591</th>\n      <td>17</td>\n      <td>女</td>\n      <td>4.01</td>\n      <td>74.0</td>\n      <td>9.67</td>\n      <td>68.0</td>\n      <td>165.0</td>\n      <td>70.0</td>\n      <td>10.0</td>\n      <td>68.0</td>\n      <td>41.0</td>\n      <td>78.0</td>\n      <td>1829.0</td>\n      <td>60.0</td>\n      <td>154.0</td>\n      <td>43.6</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>592</th>\n      <td>17</td>\n      <td>女</td>\n      <td>4.48</td>\n      <td>50.0</td>\n      <td>9.09</td>\n      <td>74.0</td>\n      <td>180.0</td>\n      <td>80.0</td>\n      <td>10.0</td>\n      <td>68.0</td>\n      <td>46.0</td>\n      <td>85.0</td>\n      <td>2962.0</td>\n      <td>85.0</td>\n      <td>162.0</td>\n      <td>55.3</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>593 rows × 17 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 73
    }
   ],
   "source": [
    "list_name_girl = ['班级','性别','女800米跑','女800米跑分数','女50米跑','女50米跑分数','女跳远','女跳远分数','女体前屈','女体前屈分数','女仰卧','女仰卧分数','女肺活量','女肺活量分数','身高','体重','BMI']\n",
    "\n",
    "data_girl=chengji_girl[list_name_girl]\n",
    "\n",
    "data_girl.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}